Field
Embodiments disclosed herein relate generally to gated computed tomography (CT) imaging systems and, more particularly, to cardiac-gated CT imaging systems.
Description of the Related Art
Computed tomography (CT) systems and methods are widely used, particularly for medical imaging and diagnosis. CT systems generally create images of one or more sectional slices through a subject's body. A radiation source, such as an X-ray source, irradiates the body from one side. A collimator, generally adjacent to the X-ray source, limits the angular extent of the X-ray beam, so that radiation impinging on the body is substantially confined to a cone-beam/fan-beam region (i.e., an X-ray projection volume) defining an image volume of the body. At least one detector (and generally many more than one detector) on the opposite side of the body receives radiation transmitted through the body substantially in the projection volume. The attenuation of the radiation that has passed through the body is measured by processing electrical signals received from the detector.
Making projective measurements at a series of different projection angles through the body, a sinogram can be constructed from the projection data, with the spatial dimension of the detector array along one axis and the time/angle dimension along the other axis. In parallel beam CT, the attenuation resulting from a particular volume within the body will trace out a sine wave oscillating along the spatial dimension of the sinogram, with the sine wave being centered on the axis of rotation for the CT system.
The process of X-ray projection measurements of the three-dimensional object onto a two-dimensional measurement plane (or a two-dimensional object onto a one-dimensional measurement plane) can be represented mathematically as a Radon transformationg(X,Y)=R[f(x,y,z)],where g(X,Y) is the projection data as a function of position along a detector array, f(x,y,z) is the attenuation of the object as a function of position, and R[•] is the Radon transform. Having measured projection data at multiple angles, the image reconstruction problem can be expressed by calculating the inverse Radon transformation of the projection dataf(x,y,z)=R−1[g(X,Y,θ)],where R−1 [•] is the inverse Radon transform and θ is the projection angle at which the projection data was acquired. In practice, there are many methods for reconstructing an image f(x,y,z) from the projection data g(X,Y,θ).
Often the image reconstruction problem will be formulated as a matrix equationAf=g, where g represents the projection measurements of the X-rays transmitted through an object space including the object OBJ, A is the system matrix describing the discretized line integrals (i.e., the Radon transforms) of the X-rays through the object space, and f is the image of object OBJ (i.e., the quantity to be solved for by solving the system matrix equation). The image f is a map of the attenuation as a function of position. Image reconstruction can be performed by taking the matrix inverse or pseudo-inverse of the matrix A. However, this rarely is the most efficient method for reconstructing an image. The more conventional approach is called filtered back-projection (FBP), which, consistent with the name, entails filtering the projection data and then back-projecting the filtered projection data onto the image space, as expressed byf(x,y,z)=BP[g(X,Y,θ)*FRamp(X,Y)].where FRamp (X,Y) is a ramp filter (the name “ramp filter” arises from its shape in the spatial-frequency domain), the symbol * denotes convolution, and BP[•] is the back projection function.
Cardiac CT presents particular challenges because, unlike other organs, the heart is constantly pumping to keep blood circulating. Therefore, special methods have developed to perform CT with short time resolution to capture an approximately stationary image of the heart. For example, the advent of subsecond rotation speeds combined with multi-slice CT (up to 320-slices) has enabled high resolution and high-speed CT imaging to be achieve simultaneously, allowing excellent imaging of the coronary arteries (cardiac CT angiography).
As a second example, images with an even higher temporal resolution can be formed using retrospective ECG gating. In this technique, each portion of the heart is imaged more than once while an ECG trace is recorded. The ECG is then used to correlate the CT data with their corresponding phases of cardiac contraction. Once this correlation is complete, all data that were recorded while the heart was in motion (systole) can be ignored and images can be made from the remaining data that happened to be acquired while the heart was at rest (diastole). In this way, individual frames in a cardiac CT investigation have a better temporal resolution than the shortest rotation time, and can even be shorter than the half-scan rotation time.
Even without ECG gating, the temporal resolution of cardiac CT has been aided by the development of short-scan reconstruction methods that do not require projection data corresponding to a complete revolution in order to reconstruct a tomographic image. For example, better time resolution time can be achieved using a half-scan reconstruction, which uses a scan of projection angles spanning 180°+2γ, where γ is the half fan angle of the cone-beam/fan-beam. The image reconstruction methods for half-scan CT reconstruction generally differ from full-scan CT reconstruction due to unequal data redundancy for projection rays through the imaged object. Whereas full-scan CT image reconstruction uses conventional FBP, wherein all projection angles are given equal weight, short-scan CT image reconstruction weights each projection angle uniquely, to compensate for unequal sampling of the image object by the measured rays—i.e., unequal data redundancy. This weighting of the projection data in the reconstruction process can be expressed asf(x,y,z)=BP[w(X,Y,θ)g(X,Y,θ)*FRamp(X,Y)],wherein w(X,Y,θ) is the weighting function. There are various approaches to account for variations in the data redundancy, including: the Dreike-Boyd parallel rebinning algorithms, complementary rebinning algorithms, applying suitable weighting function such as the Parker weights to the sinogram, and hybrid techniques.
Similar to the short-scan reconstruction, reconstructing a tomographic image using ECG gating can also involve weighting the projection data to compensate for unequal redundancy in the data.
Even though short-scan and ECG-gated reconstruction have advantages in better temporal resolution, conventional methods of short-scan and ECG-gated reconstruction can also suffer from certain artifacts, such as the banding artifact and low-frequency shading artifacts commonly found in cone-beam reconstruction, as well as poor signal-to-noise ratios (SNR).